# From 15 Experimental Models to 3 Core Solutions: A Practical Analysis of a Machine Learning-Driven Network Intrusion Detection System

> This article provides an in-depth analysis of a cyber attack detection project transitioning from academic research to production environments, demonstrating how to select three core models (CNN, LSTM, and LightGBM) through systematic experiments and offer optimal solutions for different scenarios.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-09T21:15:41.000Z
- 最近活动: 2026-06-09T21:18:11.640Z
- 热度: 160.0
- 关键词: 网络安全, 入侵检测, 机器学习, 深度学习, CNN, LSTM, LightGBM, 异常检测
- 页面链接: https://www.zingnex.cn/en/forum/thread/153
- Canonical: https://www.zingnex.cn/forum/thread/153
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: From 15 Experimental Models to 3 Core Solutions: A Practical Analysis of a Machine Learning-Driven Network Intrusion Detection System

This article provides an in-depth analysis of a cyber attack detection project transitioning from academic research to production environments, demonstrating how to select three core models (CNN, LSTM, and LightGBM) through systematic experiments and offer optimal solutions for different scenarios.

## Original Author and Source

- **Original Author/Maintainer:** Nefise Turgut
- **Source Platform:** GitHub
- **Original Title:** cyber-attack-detection-ai
- **Original Link:** https://github.com/nefiseturgut/cyber-attack-detection-ai
- **Publication Date:** June 2026

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## Project Background and Motivation

In today's increasingly digital world, the frequency and complexity of cyber attacks are on the rise. Traditional rule-based Intrusion Detection Systems (IDS) often struggle to handle new attack methods, while machine learning technology brings new possibilities to this field.

This project was developed by Nefise Turgut, a computer engineering student, as her graduation project. Initially, it was a comprehensive research project covering 15 different models. Through systematic experimental comparisons, three core models most suitable for production environments were finally selected, forming a complete cyber attack detection solution.

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## Dataset Selection and Characteristics

The project uses three representative public datasets, covering the full spectrum from classic benchmark tests to modern real attack scenarios:

## KDD Cup 1999

As the most classic benchmark dataset in the field of intrusion detection, it contains about 500,000 records and 41 features. Although it is old, it is still a standard test set for verifying the basic capabilities of algorithms.

## CICIDS2018

A modern dataset released by the Canadian Institute for Cybersecurity, containing about 1 million records and 80 features, covering current mainstream cyber attack types and better reflecting the complexity of real network environments.

## UNSW-NB15

A more balanced and realistic dataset with about 257,000 records and 42 features, achieving a good balance between academic research and industrial applications.

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## In-depth Analysis of the Three Core Models

After a comprehensive evaluation of 15 experimental models, the project finally identified three core models, each targeting different application scenarios: